Title: Causal-effect structure transformation based on hierarchical representation for biomedical sensing
Authors: Naoki Tsuchiya, Mitsuhiro Yoneda, Hiroshi Nakajima
Addresses: Core Technology Center, OMRON Corporation, 9-1 Kizugawadai, Kizugawa-City, Kyoto 619-0283, Japan. ' Core Technology Center, OMRON Corporation, 9-1 Kizugawadai, Kizugawa-City, Kyoto 619-0283, Japan. ' Core Technology Center, OMRON Corporation, 9-1 Kizugawadai, Kizugawa-City, Kyoto 619-0283, Japan
Abstract: In general, understanding causality among components in a target system, including a human body, is quite effective and efficient solution since utilisation of the causality helps predicting future system condition, making correct diagnosis and so forth. As for focusing on biomedical sensing, the causality among vital signals obtained from sensors built in measurement equipment should be considered to recognise human|s health condition correctly. In addition, effective causality transformation is desired when measurement equipment is improved such as replacing or maintaining components in the equipment. In this article, causality transformation method for improving causality is proposed. It employs a hierarchical representation of the causality based on human-machine collaborative knowledge and its applications of visceral fat area estimation and heart rate monitoring are presented.
Keywords: causal structure transformation; causal hierarchical representation; human-machine collaboration; biomedical sensing; human-machine interaction; vital signals; human health; causality; visceral fat area estimation; heart rate monitoring.
World Review of Science, Technology and Sustainable Development, 2010 Vol.7 No.1/2, pp.116 - 129
Published online: 31 Mar 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article